Earlier this year, we empowered over 10,000 students from all over the world to learn the basics of machine learning over the course of four months. We are excited to announce the next stage of skilling with the availability of an advanced machine learning nanodegree on Udacity. Starting today, students can enroll for the Machine Learning Engineer for Microsoft Azure Nanodegree Program.
This new nanodegree program offers students the opportunity to develop deeper technical skills in machine learning (ML). Students will strengthen their skills by building and deploying sophisticated ML models using Azure Machine Learning. They will learn how to train ML models, manage ML pipelines, and tune hyperparameters to improve model performance. Once the model is ready, students will learn how to operationalize the model with the right MLOps practices, including automation, CI/CD, and monitoring.
Students will get hands-on exposure with built-in Azure labs that are designed to help students put theory into practice, all within Udacity’s classroom environment. To round it up, students will have the opportunity to show off their talents by completing a capstone project based on a real-life data science scenario. By the end of this program, students will also be well-prepared to earn
Data is growing exponentially, and over 80 percent of data is unstructured1, creating a challenge for organizations to find and surface the right information to their customers. What organizations need is a solution that enables them to uncover latent insights from all their content by quickly identifying relevant information and meaningful patterns.
Knowledge mining is a category in AI that brings together multiple AI capabilities, making it easier for developers to get to insights faster. Azure Cognitive Search powers knowledge mining solutions to easily identify and explore relevant content at scale.
With Azure Cognitive Search, cloud search has evolved to include AI capabilities, across ingestion, enrichment, and exploration of structured and unstructured content. With APIs and tools, developers can build solutions that power rich search experiences over a variety of content in web, mobile, and enterprise applications.
Customers across multiple industries have leveraged Azure Cognitive Search to deliver enhanced app search experience. Amway has used Azure Cognitive Search to power applications and help sellers better explore and understand product information. As a result, search time for sellers reduced significantly, empowering them to help four times more customers every day. The Atlantic utilized Azure Cognitive Search to transition to
Organizations today are striving to build agility and resilience to the fast-changing environment we live in. AI and machine learning innovation can help tackle these emerging challenges and enable cost efficiencies. However, organizations still encounter barriers to adopting and deploying machine learning at scale. Recently at Microsoft Ignite, Azure Machine Learning made a number of announcements that help organizations harness machine learning more easily, securely, and at scale. This includes capabilities like designer and automated machine learning UI, now generally available, that simplify machine learning for beginners and professionals alike. Advanced role-based access control (RBAC) and private IP link, in preview, make it possible to build machine learning solutions more securely. In addition, we are merging the Azure Machine Learning Enterprise and Basic Editions to deliver greater value at no extra cost.
“With Azure Machine Learning, we’re increasing speed-to-value while reducing cost-to-value.” – Sarah Dods, Head of Advanced Analytics, AGL. Read the story.
Machine learning simplified
Azure Machine Learning designer provides a drag-and-drop canvas to build no-code models with ease. Built-in modules help preprocess data and build and train models using machine learning and deep learning algorithms, including computer vision, text analytics, recommendation, anomaly detection, and
As organizations assess safely reopening and continue navigating unexpected shifts in the world, getting insights to respond in an agile and conscientious manner is vital. Developers and data scientists of all skill levels are inventing with Microsoft Azure AI’s powerful and responsible tools to meet these challenges.
To help organizations operate safely in today’s environment, we are introducing a new spatial analysis capability in the Computer Vision Azure Cognitive Service. Its advanced AI models aggregate insights from multiple cameras to count the number of people in the room, measure the distance between individuals, and monitor wait and dwell times. Organizations can now apply this technology to use their space in a safe, optimal way. For instance, RXR, one of New York City’s largest real estate companies, has embedded spatial analysis in their RxWell app to ensure occupants’ safety and wellness.
“When it came to developing RxWell, there was simply no other company that had the capability and the infrastructure to meet our comprehensive data, analytics, and security needs than Microsoft. With our partnership, the RxWell program provides our customers the tools they need to safely navigate the ‘new abnormal’ of COVID-19 and beyond.” – Scott Rechler,
In early 2020, Frost & Sullivan recognized Microsoft as the “undisputed leader” in global Artificial Intelligence (AI) platforms for the Healthcare IT (HCIT) sector on the Frost Radar™. In a field of more than 200 global industry participants, Frost & Sullivan independently plotted the top 20 companies across various parameters indicative of growth and innovation, available for consumption here.
According to Frost & Sullivan, the global AI HCIT market is on a rapid growth trajectory, with sales of AI-enabled HCIT products expected to generate more than $34.83 billion globally by 2025. Government agencies will contribute almost 50.7 percent of the revenue (including public payers), followed by hospital providers (36.3 percent) and physician practices (13 percent). Clinical AI solutions will drive 40 percent of the market revenue, with financial AI solutions contributing the same, and the remaining 20 percent coming from sales of operational AI solutions. Globally, Microsoft earned the top spot because of its industry-leading effort to incorporate next-generation AI infrastructure to drive precision medicine workflows, aid population health analytics, propel evidence-based clinical research, and expedite drug and treatment discovery.
Figure 1: The Frost Radar, “Global AI for Healthcare IT Market”, 2020
We’re seeing providers deploy chatbots in their
The trend toward the use of massive AI models to power a large number of tasks is changing how AI is built. At Microsoft Build 2020, we shared our vision for AI at Scale utilizing state-of-the-art AI supercomputing in Azure and a new class of large-scale AI models enabling next-generation AI. The advantage of large scale models is that they only need to be trained once with massive amounts of data using AI supercomputing, enabling them to then be “fine-tuned” for different tasks and domains with much smaller datasets and resources. The more parameters that a model has, the better it can capture the difficult nuances of the data, as demonstrated by our 17-billion-parameter Turing Natural Language Generation (T-NLG) model and its ability to understand language to answer questions from or summarize documents seen for the first time. Natural language models like this, significantly larger than the state-of-the-art models a year ago, and many orders of magnitude the size of earlier image-centric models, are now powering a variety of tasks throughout Bing, Word, Outlook, and Dynamics.
Training models at this scale requires large clusters of hundreds of machines with specialized AI accelerators interconnected by high-bandwidth networks inside and across the machines.
Climate experts across the globe agree: if we can’t drastically reduce carbon emissions, our planet will face catastrophic consequences. Microsoft has operated carbon neutral since 2012, and in January 2020 Brad Smith announced our commitment to going carbon negative by 2030. This isn’t a goal we can reach in one easy swoop—it will take time, dedication, and many small steps that coalesce into something greater.
As the cloud business grows, our datacenter footprint grows. In our journey toward carbon negative, Microsoft is taking steps to roll back the effect datacenters have on the environment. Reaching this goal will take many steps, along with the implementation of innovative technologies that have yet to be developed.
Many companies are reaching for net zero emissions, but we’re taking it even further. We’re not just reducing our output to zero. We’re committed to reducing our emissions by half, and then removing the carbon we’ve emitted since 1975, to truly go carbon negative.
The journey to carbon negative
A big part of going carbon negative means completely changing the way datacenters operate. Datacenters have adopted some sustainable methods around cooling, including open-air and adiabatic cooling. These methods have helped to drastically reduce the water and
As the world adjusts to new ways of working and staying connected, we remain committed to providing Azure AI solutions to help organizations invent with purpose.
Building on our vision to empower all developers to use AI to achieve more, today we’re excited to announce expanded capabilities within Azure Cognitive Services, including:.
Text Analytics for health preview. Form Recognizer general availability. Custom Commands general availability. New Neural Text to Speech voices.
Companies in healthcare, insurance, sustainable farming, and other fields continue to choose Azure AI to build and deploy AI applications to transform their businesses. According to IDC1, by 2022, 75 percent of enterprises will deploy AI-based solutions to improve operational efficiencies and deliver enhanced customer experiences.
To meet this growing demand, today’s product updates expand on existing language, vision, and speech capabilities in Azure Cognitive Services to help developers build mission-critical AI apps that enable richer insights, save time and reduce costs, and improve customer engagement.
Get rich insights with powerful natural language processing
One of the ways organizations are adapting is scaling the ability to rapidly process data and generate new insights from data. COVID-19 has accelerated the urgency, particularly for the healthcare industry. With the overwhelming amount
“In the era of big data, insights collected from cloud services running at the scale of Azure quickly exceed the attention span of humans. It’s critical to identify the right steps to maintain the highest possible quality of service based on the large volume of data collected. In applying this to Azure, we envision infusing AI into our cloud platform and DevOps process, becoming AIOps, to enable the Azure platform to become more self-adaptive, resilient, and efficient. AIOps will also support our engineers to take the right actions more effectively and in a timely manner to continue improving service quality and delighting our customers and partners. This post continues our Advancing Reliability series highlighting initiatives underway to keep improving the reliability of the Azure platform. The post that follows was written by Jian Zhang, our Program Manager overseeing these efforts, as she shares our vision for AIOps, and highlights areas of this AI infusion that are already a reality as part of our end-to-end cloud service management.”—Mark Russinovich, CTO, Azure
This post includes contributions from Principal Data Scientist Manager Yingnong Dang and Partner Group Software Engineering Manager Murali Chintalapati.
As Mark mentioned when he launched this Advancing Reliability blog
Machine learning (ML) is gaining momentum across a number of industries and scenarios as enterprises look to drive innovation, increase efficiency, and reduce costs. Microsoft Azure Machine Learning empowers developers and data scientists with enterprise-grade capabilities to accelerate the ML lifecycle. At Microsoft Build 2020, we announced several advances to Azure Machine Learning across the following areas: ML for all skills, Enterprise grade MLOps, and responsible ML.
ML for all skills
New enhancements provide ML access for all skills.
Enhanced notebook in preview
Data scientists and developers can now access an enhanced notebook editor directly inside Azure Machine Learning studio. New capabilities to create, edit, and collaborate make remote work and sharing easier for data science teams and the notebook is fully compatible with Jupyter.
Boost development productivity with features like IntelliSense, inline error highlighting, and code suggestions from VSCode, which deliver the best-in-class coding experience in Jupyter notebooks. Access real-time co-editing (coming soon) for seamless remote collaboration or pair debugging. Inline controls to start, stop, and create a new compute using GPU or CPU Compute Instance inside notebooks. Add new kernels to the notebook editor and quickly switch between different kernels like Python and R.
Real-time notebook co-editing